Bibliographic record
Abstract
I first met Peter in the early 1970s.I knew of him mainly because he, like me, was a veterinarian who did research and published in international nonveterinary journals.Upon meeting him, which initially occurred when he passed through Saskatoon, Canada, where I had my laboratory in the early 1970s, it was soon apparent that neither of us were cut out to practice our intended craft of fixing diseased creatures.Instead, we were interested in viruses and figuring out how they interacted with their host.Nevertheless, both of us began our research careers working with real animals (i.e., those you eat or become fond of), but soon slipped to working with rodents.Over the years, I met Peter frequently, our families became and remain lifelong friends and I also got to know many, perhaps most, of Peter's trainees.These included some quite amazing characters such as Ralph Tripp and more sane folk such as Woody, Jack Bennink, Rhonda Cardin, Steve Turner, Mark Sangster, and many more.From the earliest years, Peter has been a role model for me.I always envied his common sense understanding of science and his ability to ask penetrating questions often criticizing people and their ideas without them realizing it.On the contrary, I always succeeded in insulting people even when I was trying to be nice!In the 1970s, I got to listen to many of Peter's talks and review some of his grants.Unlike the lucid Peter of today, his scientific stories were not the easiest to follow and his experimentation could be beyond the pale complex.Peter departed the Wistar where he spent several years solidifying his reputation as a viral immunologist and returned to Canberra to head up the Australian National University Department of Pathology.I joined him there for a minisabbatical in 1986.It was obvious that Peter enjoyed bench science but almost despised administration and other administrators (such as Bede Morris, also a veterinarian).He wanted to leave Canberra (''best part was the road out to Sydney'') and return to the United States.I worked along with Peter dutifully counting cells in Cerebrospinal fluid samples he collected from lymphocytic choriomeningitis virus infected mice.I measured a few things and we wrote our only article together.It was not a citation classic!My biggest achievement while in Canberra was to encourage Peter to return to the United States, which eventually, after much agonizing, became lucky Tennessee-my own adopted state.Alas, he chose to locate at the intellectual end of Tennessee-St.Jude in Memphis, whereas I resided in the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".